AgentCL: Toward Rigorous Evaluation of Continual Learning in Language Agents
Language agents spend substantial inference time solving individual tasks, yet the experience acquired in one episode is often underutilized in future episodes. Continual learning expects an agent to accumulate experience across a stream of tasks, improve over time, and avoid interference from irrelevant experiences. Unfortunately, existing benchmarks struggle to evaluate this problem rigorously. Most efforts focus on retrieval and reasoning over long-context conversations or documents, while recent lifelong-adaptation benchmarks often stream existing datasets whose cross-task dependencies are unknown, making it difficult to understand what an agent learns and reuses over time. This paper...
| Authors | Yiheng Shu, Bernal Jiménez Gutiérrez, Saisri Padmaja Jonnalagedda et al. |
|---|---|
| Primary category | cs.AI |
| Published | 1 June 2026 |
| Last updated at source | 26 September 2026 |
Models and datasets referencing this paper
- osunlp/AgentCLDataset
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